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Gold is another kind of investment that often experiences price change, mostly every day. Because of its price fluctuation, forecasting is needed to help the investor in investment decision making. But during this Coronavirus Disease 2019 (Covid-19), the gold price is fluctuating extremely than the past 4 years so a better forecasting method approached and analysis technique is needed due to this case. Double Exponential Smoothing method is chosen to forecast this daily gold price. On the other hand, there are so many missing values spreading around the main dataset so the imputation method is needed too, Last Observation Carried Forward (LOCF) and linear interpolation are chosen for imputing the missing values. In this research, the main dataset was split into 3 (three) datasets, which are Precovid-19 (before Covid-19, used only for visualizing the actual fluctuation condition during this pandemic), Incovid-19 (during Covid-19 based on the date where the first Covid-19 case occurred in Indonesia), and Combination (a binding dataset of Pracovid-19 and Incovid-19). Although Incovid-19’s MAPE value is higher than Pracovid-19 and Combination’s MAPE values, in evaluation session showed that Incovid-19’s MAPE of forecast results has the lowest value rather than Combination’s MAPE of forecast results, so the conclusion of this research is Incovid-19 dataset with LOCF imputation is the most adaptive with the actual condition and it is used to forecast the daily gold price until the last period of the main dataset then.
B. Lobel, "What is Gold? Understanding Gold as a Trader's Commodity," Gold Price , 25 09 2019. [Online]. Available: https://www.dailyfx.com/gold-price/what-is-gold.html. [Accessed 29 12 2020].
A. Hayes and M. J. Boyle, "Why Gold Matters: Everything You Need To Know," Investopedia, 14 9 2020. [Online]. Available: https://www.investopedia.com/articles/economics/09/why-gold-matters.asp. [Accessed 29 12 2020].
A. Zaenudin, "Menambang Emas dari Perangkat Elektronik Kita," tirto.id, 9 1 2018. [Online]. Available: https://tirto.id/menambang-emas-dari-perangkat-elektronik-kita-cCW8. [Accessed 29 12 2020].
A. N, "Can Gold Prices Forecast the Australian Dollar Movements?," International Economics and Finance, pp. 75-82, 2014.
K. Gilchrist, "Gold has surged due to the pandemic — and it could keep going. Here’s what to know about investing now," A Division of NBC Universal, 16 11 2020. [Online]. Available: https://www.cnbc.com/2020/07/20/investing-how-to-invest-in-gold-is-now-a-good-time-to-buy-gold. [Accessed 29 12 2020].
S. Zhou, K. K. Lai and J. Yen, "A Dynamic Meta-Learning rate-Based Model for Gold Market Forecasting," Expert Systems with Applications, 2012.
Gold Forecast, "Gold Price," DailyFX, 29 12 2020. [Online]. Available: https://www.dailyfx.com/gold-price. [Accessed 29 12 2020].
C. Aaron, "Gold Price 2021 Forecast: Continuation Advance Ahead," Latest Gold Price Forecast & Predictions, 24 12 2020. [Online]. Available: https://www.gold-eagle.com/forecasts_predictions. [Accessed 29 12 2020].
J. Clark, S. Analyst and GoldSilver, "2020 Gold Price Forecast, Trends, & 5 Year Predictions," Gold Silver, 2020. [Online]. Available: https://goldsilver.com/blog/gold-price-forecast-predictions/. [Accessed 29 12 2020].
The Economy Forecast Agency, "Gold Price Forecast For 2020, 2021, 2022, 2023 And 2024," Forecast Agency, 12 2020. [Online]. Available: https://longforecast.com/gold-price-today-forecast-2017-2018-2019-2020-2021-ounce-gram. [Accessed 2020 12 2020].
R. S. Nugroho, "Rekap Kasus Corona Indonesia Selama Maret dan Prediksi di Bulan April," Kompas, 31 Maret 2020. [Online]. Available: https://amp.kompas.com/tren/read/2020/03/31/213418865/rekap-kasus-corona-indonesia-selama-maret-dan-prediksi-di-bulan-april. [Accessed 29 12 2020].
T. Woodall, "Gold price set to lose momentum beyond pandemic after 28% increase in 2020," S&P Global, 21 9 2020. [Online]. Available: https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/gold-price-set-to-lose-momentum-beyond-pandemic-after-28-increase-in-2020-60056128. [Accessed 29 12 2020].
A. C. R and S. Sopia, "Menimbang Urgensi Berinvestasi Kala Pandemi," Republika, 11 4 2020. [Online]. Available: https://www.republika.id/posts/5837/menimbang-urgensi-berinvestasi-kala-pandemi.. [Accessed 29 12 2020].
K. Safitri and B. P. Jatmiko, "Perhatikan Ini, Jika Ingin Investasi Emas Saat Pandemi Covid-19," Kompas.com, 30 4 2020. [Online]. Available: https://money.kompas.com/read/2020/04/30/125434126/perhatikan-ini-jika-ingin-investasi-emas-saat-pandemi-covid-19. [Accessed 29 12 2020].
Bisnis.com, "Baru Mau Investasi Emas di Masa Pandemi? 'Mending' Tahan Dulu!," Finansial.Bisnis.com, 22 5 2020. [Online]. Available: https://finansial.bisnis.com/read/20200522/55/1243657/baru-mau-investasi-emas-di-masa-pandemi-mending-tahan-dulu. [Accessed 29 12 2020].
L. A. Azanella and S. Hardiyanto, "Emas Cenderung Naik Saat Terjadi Ketidakpastian Ekonomi, Mengapa?," Kompas.com, 3 8 2020. [Online]. Available: https://www.kompas.com/tren/read/2020/08/03/125600165/emas-cenderung-naik-saat-terjadi-ketidakpastian-ekonomi-mengapa-?page=all. [Accessed 29 12 2020].
I. Priyadi, J. Santony and J. Na'am, "Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices," Indonesian Journal of Artificial Intelligence and Data Mining, vol. 2, no. 2, pp. 93-100, 2019.
M. Riazuddin, "Predicting Gold Prices Using Machine Learning," Gold Prediction Series, 15 5 2020. [Online]. Available: https://towardsdatascience.com/machine-learning-to-predict-gold-price-returns-4bdb0506b132. [Accessed 29 12 2020].
A. Hatamlou and M. Deljavan, "Forecasting Gold Price using Data Mining Techniques by Considering New," Journal of AI and Data Mining, vol. 7, no. 3, pp. 411-420, 2018.
"Methods and formulas for Double Exponential Smoothing," Minitab, 2019. [Online]. Available: https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/time-series/how-to/double-exponential-smoothing/methods-and-formulas/methods-and-formulas/. [Accessed 29 12 2020].
SCRC SME, "Double Exponential Smoothing: Approaches to Forecasting : A Tutorial," NC State University, 25 1 2011. [Online]. Available: https://scm.ncsu.edu/scm-articles/article/double-exponential-smoothing-approaches-to-forecasting-a-tutorial. [Accessed 29 12 2020].
Logam Mulia, "Harga Emas," Logam Mulia, 2020. [Online]. Available: https://www.logammulia.com/id/harga-emas-hari-ini. [Accessed 2020 12 2020].
H. Xu and I. Biogenidec, "LOCF Method and Application in Clinical Data Analysis," pp. 1-5, 2009.
X. Liu, Methods for handling missing data, Vols. 978-0-12-801342-7, Elsevier Inc. All rights reserved., 2016.
A. M. Bayen and T. Siauw, An Introduction to MATLAB® Programming and Numerical Methods for Engineers, Elsevier Inc. All rights reserved., 2015.
A. Setiaji, "Machine Learning : Missing Value," 2018. [Online]. Available: https://mragungsetiaji.github.io/python/machine%20learning/2018/08/23/machine-learning-missing-value.html. [Accessed 29 12 2020].
colab.research.google, "Bagian 1, Pengenalan Missing value," colab.research.google, [Online]. Available: https://colab.research.google.com/drive/1Z8HcT33Z8AqzGrfCnGJle4yEOE1Piugd. [Accessed 29 12 2020].
S. Z. Abidin, T. M. T. Jalal, F. A. Razali, N. H. Hassim and N. F. Haron, "Comparison on estimating Malaysia gold price via nonlinear prediction method and Box–Jenkins model," in AIP Conference Proceedings 1974, 020057 (2018), 2018.
S. Hansun, "A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis," Balkan Journal of Elekctrical & Computer Engineering, vol. 4, no. 2, pp. 75-78, 2016.
S. Hansun and Subanar, "H-WEMA: A New Approach of Double Exponential Smoothing Method," TELKOMNIKA, vol. 14, no. 2, pp. 772-777, 2016.
B. Abraham and J. Ledolter, "Regression and Exponential Smoothing Methods to Forecast Nonseasonal Time Series," Statistical Methods for Forecasting, pp. 79-134, 2008.
D. Mavridis, G. Salanti, T. A. Furukawa, A. Cipriani, A. Chaimani and I. R. White, "Allowing for uncertainty due to missing and LOCF imputedoutcomes in meta-analysis," Statistics in Medicine, pp. 1-18, 2018.
D. Banerjee and A. Ghosal, "A Novel Technique to Utilize Geopolitical Risk as a Factor for Predicting Gold Price," in Computational Intelligence and Machine Learning, Proceedings of the 7th International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019), 2019.
D. M. North, Data Mining for the Masses, Global Text Project 2012, 2012.
A. Andiojaya and H. Demirhan, "A Bagging Algorithm for the Imputation of Missing Values in Time Series," Expert Systems with Applications, pp. 10-26, 2019.
R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data, New York: J. Wiley & Sons, 1987.
T. Hendrawati, "Kajian Metode Imputasi dalam Menangani Missing Data," in Prosiding Seminar Nasional Matematika dan Pendidikan Matematika UMS, Surakarta, 2015.
Muflihah and R. Y. Pahlawan, "Perbandingan Teknik Interpolasi Berbasis R dalam Estimasi Data Curah Hujan Bulanan yang Hilang di Sulawesi," Puslitbang BMKG, Jakarta, 2017.
T.-T.-T. Phan, É. P. Caillault, A. Lefebvre and A. Bigand, "Dynamic Time Warping-Based Imputation for Univariate Time Series Data," Pattern Recognition Letters, pp. 2-11, 2017.
M. D. Bordo and F. Capie, Monetary Regimes in Transition, Cambridge University Press, 1993.
R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, Otexts, 2013.
M. I. J. Lamabelawa, "Analisis Perhitungan Metode Interpolasi pada Data Time Series Kemiskinan di NTT," Jurnal HOAQ-Teknologi Informasi Vol. 8,, pp. 640-646, 2018.
S. Makridakis, S. C. Wheelwrigh and V. E. McGre, Metode dan aplikasi peramalan, Jakarta: Erlangga, 1999.
R. Adhikari and R. K. Agrawal, An Introductory Study on Time Series Modeling and Forecasting, Cornell University, 2013.
J. Suntoro, Data Mining : Algoritma dan Implementasi dengan Pemrograman PHP, Jakarta: PT. Elex Media Komputindo, 2019.
P.-. C. Chang, Y.-. W. Wang and C.-. H. Liu, "The Development of A Weighted Evolving Fuzzy Neural network for PCB Sales Forecasting," Expert Systems with Applications 32, p. 88, 2007.